Journal Articles
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Item EmoWare: A context-aware framework for personalized video recommendation using affective video sequences(Institute of Electrical and Electronics Engineers Inc., 2019) Tripathi, A.; Ashwin, T.S.; Guddeti, R.M.R.With the exponential growth in areas of machine intelligence, the world has witnessed promising solutions to the personalized content recommendation. The ability of interactive learning agents to make optimal decisions in dynamic environments has been proven and very well conceptualized by reinforcement learning (RL). The learning characteristics of deep-bidirectional recurrent neural networks (DBRNN) in both positive and negative time directions has shown exceptional performance as generative models to generate sequential data in supervised learning tasks. In this paper, we harness the potential of the said two techniques and propose EmoWare (emotion-aware), a personalized, emotionally intelligent video recommendation engine, employing a novel context-aware collaborative filtering approach, where the intensity of users' spontaneous non-verbal emotional response toward the recommended video is captured through interactions and facial expressions analysis for decision-making and video corpus evolution with real-time feedback streams. To account for users' multidimensional nature in the formulation of optimal policies, RL-scenarios are enrolled using on-policy (SARSA) and off-policy (Q-learning) temporal-difference learning techniques, which are used to train DBRNN to learn contextual patterns and to generate new video sequences for the recommendation. System evaluation for a month with real users shows that the EmoWare outperforms the state-of-the-art methods and models users' emotional preferences very well with stable convergence. © 2013 IEEE.Item Affective database for e-learning and classroom environments using Indian students’ faces, hand gestures and body postures(Elsevier B.V., 2020) Ashwin, T.S.; Guddeti, R.M.R.Automatic recognition of the students’ affective states is a challenging task. These affective states are recognized using their facial expressions, hand gestures, and body postures. An intelligent tutoring system and smart classroom environment can be made more personalized using students’ affective state analysis, and it is performed using machine or deep learning techniques. Effective recognition of affective states is mainly dependent on the quality of the database used. But, there exist very few standard databases for the students’ affective state recognition and its analysis that works for both e-learning and classroom environments. In this paper, we propose a new affective database for both the e-learning and classroom environments using the students’ facial expressions, hand gestures, and body postures. The database consists of both posed (acted) and spontaneous (natural) expressions with single and multi-person in a single image frame with more than 4000 manually annotated image frames with object localization. The classification was done manually using the gold standard study for both Ekman's basic emotions and learning-centered emotions, including neutral. The annotators reliably agree when discriminating against the recognized affective states with Cohen's ? = 0.48. The created database is more robust as it considers various image variants such as occlusion, background clutter, pose, illumination, cultural & regional background, intra-class variations, cropped images, multipoint view, and deformations. Further, we analyzed the classification accuracy of our database using a few state-of-the-art machine and deep learning techniques. Experimental results demonstrate that the convolutional neural network based architecture achieved an accuracy of 83% and 76% for detection and classification, respectively. © 2020 Elsevier B.V.Item Surveillance video analysis for student action recognition and localization inside computer laboratories of a smart campus(Springer, 2021) Rashmi, M.; Ashwin, T.S.; Guddeti, G.R.M.In the era of smart campus, unobtrusive methods for students’ monitoring is a challenging task. The monitoring system must have the ability to recognize and detect the actions performed by the students. Recently many deep neural network based approaches have been proposed to automate Human Action Recognition (HAR) in different domains, but these are not explored in learning environments. HAR can be used in classrooms, laboratories, and libraries to make the teaching-learning process more effective. To make the learning process more effective in computer laboratories, in this study, we proposed a system for recognition and localization of student actions from still images extracted from (Closed Circuit Television) CCTV videos. The proposed method uses (You Only Look Once) YOLOv3, state-of-the-art real-time object detection technology, for localization, recognition of students’ actions. Further, the image template matching method is used to decrease the number of image frames and thus processing the video quickly. As actions performed by the humans are domain specific and since no standard dataset is available for students’ action recognition in smart computer laboratories, thus we created the STUDENT ACTION dataset using the image frames obtained from the CCTV cameras placed in the computer laboratory of a university campus. The proposed method recognizes various actions performed by students in different locations within an image frame. It shows excellent performance in identifying the actions with more samples compared to actions with fewer samples. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.